##### ------- Zero Alpha
## listW2_alpha0 <- clusterize_MixDistW2(
## data_taxonomy = dat_tax,
## vec_aggreg = vec_aggregation,
## data_metadat = metadat,
## scalingLatDepth = T,
## ABS_Latitude = T,
## propGeo = 0,
## max_k = 50,max_at_least = 3)
##
## listMedois_alpha0 <- clusterize_MixDist_kmedoids(
## data_taxonomy = dat_tax,
## vec_aggreg = vec_aggregation,
## data_metadat = metadat,
## scalingLatDepth = T,
## ABS_Latitude = T,
## propGeo = 0,
## max_k = 50,max_at_least = 3)
##
## dat_tax_aux <- aggregating_compositions(
## dFrame = dat_tax,
## fillZeros = 'Nothing',
## aggregating_level = vec_aggregation[1],
## PresentAtLeast = 1,
## metadata = metadat
## ) %>% mutate(
## OBS=1:n()) %>%
## select(OBS,Latitude,Depth,Pressure_decibars,
## Salinity_psu,Temperature_degrees_Celsius)
##
## outputSS_ScaledABS <- list()
## kmax = length(listW2_alpha0$AtLeast1$PG)
## for (i in 1:3){
##
## outputAgg <- list()
## for(j in 1:length(vec_aggregation)){
##
## outputDistances <- list()
## for(k in 1:kmax){
##
## dat_tax_aux =
## dat_tax_aux %>% mutate(
## Ward_Clust = factor(listW2_alpha0[[i]][[j]][[k]]),
## Medoid_Clust = factor(listMedois_alpha0[[i]][[j]][[k]]))
##
## outputDistances[[k]] <- list(
## Ward=dat_tax_aux %>% geoCoherense(ClustVar = 'Ward_Clust',DephtVar = 'Depth'),
## Medoid=dat_tax_aux %>% geoCoherense(ClustVar = 'Medoid_Clust',DephtVar = 'Depth'))
## }
## names(outputDistances) <- ifelse(1:kmax < 10,
## paste('Cluster0',1:kmax,sep=''),
## paste('Cluster',1:kmax,sep=''))
## outputAgg[[j]]<-outputDistances
## rm(outputDistances)
## }
##
## names(outputAgg) <- vec_aggregation
## outputSS_ScaledABS[[i]] <- outputAgg
## rm(outputAgg)
## }
##
## names(outputSS_ScaledABS) <- paste('AtLeastIn',1:3,sep='')
##
## saveRDS(object = outputSS_ScaledABS,file = 'outputSS_ScaledABS')
## outputSS_ScaledABS=readRDS('outputSS_ScaledABS')
##
## df_outputSS_ScaledABS_a0 <- outputSS_ScaledABS %>% plyr::ldply(function(atleast){
## atleast %>% plyr::ldply(function(agglevel){
## agglevel %>% plyr::ldply(function(cluster){
## cluster %>% plyr::ldply(function(method){
## method %>% plyr::ldply(function(dimension){
## return(data.frame(TotalSum = sum(dimension$SumDist)))
## }, .id = "dimension")
## }, .id = "method")
## }, .id = "clusters")
## }, .id = "agglevel")
## }, .id = "atleast") %>% mutate(AlphaGeo = 0)
##
## saveRDS(object = df_outputSS_ScaledABS_a0,file = 'df_outputSS_ScaledABS_a0')
## ######## ----------
## ##### ------- alphaGeo = 0.1
##
## listW2_alpha0.1 <- clusterize_MixDistW2(
## data_taxonomy = dat_tax,
## vec_aggreg = vec_aggregation,
## data_metadat = metadat,
## scalingLatDepth = T,
## ABS_Latitude = T,
## propGeo = 0.1,
## max_k = 50,max_at_least = 3)
##
## listMedois_alpha0.1 <- clusterize_MixDist_kmedoids(
## data_taxonomy = dat_tax,
## vec_aggreg = vec_aggregation,
## data_metadat = metadat,
## scalingLatDepth = T,
## ABS_Latitude = T,
## propGeo = 0.1,
## max_k = 50,max_at_least = 3)
##
## dat_tax_aux <- aggregating_compositions(
## dFrame = dat_tax,
## fillZeros = 'Nothing',
## aggregating_level = vec_aggregation[1],
## PresentAtLeast = 1,
## metadata = metadat
## ) %>% mutate(
## OBS=1:n()) %>%
## select(OBS,Latitude,Depth,Pressure_decibars,
## Salinity_psu,Temperature_degrees_Celsius)
##
## outputSS_ScaledABS_a0.1 <- list()
## kmax = length(listMedois_alpha0.1$AtLeast1$PG)
## for (i in 1:3){
##
## outputAgg <- list()
## for(j in 1:length(vec_aggregation)){
##
## outputDistances <- list()
## for(k in 1:kmax){
##
## dat_tax_aux =
## dat_tax_aux %>% mutate(
## Ward_Clust = factor(listW2_alpha0.1[[i]][[j]][[k]]),
## Medoid_Clust = factor(listMedois_alpha0.1[[i]][[j]][[k]]))
##
## outputDistances[[k]] <- list(
## Ward=dat_tax_aux %>% geoCoherense(ClustVar = 'Ward_Clust',DephtVar = 'Depth'),
## Medoid=dat_tax_aux %>% geoCoherense(ClustVar = 'Medoid_Clust',DephtVar = 'Depth'))
## }
## names(outputDistances) <- ifelse(1:kmax < 10,
## paste('Cluster0',1:kmax,sep=''),
## paste('Cluster',1:kmax,sep=''))
## outputAgg[[j]]<-outputDistances
## rm(outputDistances)
## }
##
## names(outputAgg) <- vec_aggregation
## outputSS_ScaledABS_a0.1[[i]] <- outputAgg
## rm(outputAgg)
## }
##
## names(outputSS_ScaledABS_a0.1) <- paste('AtLeastIn',1:3,sep='')
##
## saveRDS(object = outputSS_ScaledABS_a0.1,file = 'outputSS_ScaledABS_a0.1')
## outputSS_ScaledABS_a0.1=readRDS('outputSS_ScaledABS_a0.1')
##
## df_outputSS_ScaledABS_a0.1 <- outputSS_ScaledABS_a0.1 %>% plyr::ldply(function(atleast){
## atleast %>% plyr::ldply(function(agglevel){
## agglevel %>% plyr::ldply(function(cluster){
## cluster %>% plyr::ldply(function(method){
## method %>% plyr::ldply(function(dimension){
## return(data.frame(TotalSum = sum(dimension$SumDist)))
## }, .id = "dimension")
## }, .id = "method")
## }, .id = "clusters")
## }, .id = "agglevel")
## }, .id = "atleast") %>% mutate(AlphaGeo = 0.1)
##
## saveRDS(object = df_outputSS_ScaledABS_a0.1,file = 'df_outputSS_ScaledABS_a0.1')
##### ------- alphaGeo = 0.25
## listW2_alpha0.25 <- clusterize_MixDistW2(
## data_taxonomy = dat_tax,
## vec_aggreg = vec_aggregation,
## data_metadat = metadat,
## scalingLatDepth = T,
## ABS_Latitude = T,
## propGeo = 0.25,
## max_k = 50,max_at_least = 3)
##
## listMedois_alpha0.25 <- clusterize_MixDist_kmedoids(
## data_taxonomy = dat_tax,
## vec_aggreg = vec_aggregation,
## data_metadat = metadat,
## scalingLatDepth = T,
## ABS_Latitude = T,
## propGeo = 0.25,
## max_k = 50,max_at_least = 3)
##
## dat_tax_aux <- aggregating_compositions(
## dFrame = dat_tax,
## fillZeros = 'Nothing',
## aggregating_level = vec_aggregation[1],
## PresentAtLeast = 1,
## metadata = metadat
## ) %>% mutate(
## OBS=1:n()) %>%
## select(OBS,Latitude,Depth,Pressure_decibars,
## Salinity_psu,Temperature_degrees_Celsius)
##
## outputSS_ScaledABS_a0.25 <- list()
## kmax = length(listMedois_alpha0.25$AtLeast1$PG)
## for (i in 1:3){
##
## outputAgg <- list()
## for(j in 1:length(vec_aggregation)){
##
## outputDistances <- list()
## for(k in 1:kmax){
##
## dat_tax_aux =
## dat_tax_aux %>% mutate(
## Ward_Clust = factor(listW2_alpha0.25[[i]][[j]][[k]]),
## Medoid_Clust = factor(listMedois_alpha0.25[[i]][[j]][[k]]))
##
## outputDistances[[k]] <- list(
## Ward=dat_tax_aux %>% geoCoherense(ClustVar = 'Ward_Clust',DephtVar = 'Depth'),
## Medoid=dat_tax_aux %>% geoCoherense(ClustVar = 'Medoid_Clust',DephtVar = 'Depth'))
## }
## names(outputDistances) <- ifelse(1:kmax < 10,
## paste('Cluster0',1:kmax,sep=''),
## paste('Cluster',1:kmax,sep=''))
## outputAgg[[j]]<-outputDistances
## rm(outputDistances)
## }
##
## names(outputAgg) <- vec_aggregation
## outputSS_ScaledABS_a0.25[[i]] <- outputAgg
## rm(outputAgg)
## }
##
## names(outputSS_ScaledABS_a0.25) <- paste('AtLeastIn',1:3,sep='')
##
## saveRDS(object = outputSS_ScaledABS_a0.25,file = 'outputSS_ScaledABS_a0.25')
## outputSS_ScaledABS_a0.25=readRDS('outputSS_ScaledABS_a0.25')
##
## df_outputSS_ScaledABS_a0.25 <- outputSS_ScaledABS_a0.25 %>% plyr::ldply(function(atleast){
## atleast %>% plyr::ldply(function(agglevel){
## agglevel %>% plyr::ldply(function(cluster){
## cluster %>% plyr::ldply(function(method){
## method %>% plyr::ldply(function(dimension){
## return(data.frame(TotalSum = sum(dimension$SumDist)))
## }, .id = "dimension")
## }, .id = "method")
## }, .id = "clusters")
## }, .id = "agglevel")
## }, .id = "atleast") %>% mutate(AlphaGeo = 0.25)
##
## saveRDS(object = df_outputSS_ScaledABS_a0.25,file = 'df_outputSS_ScaledABS_a0.25')